Vineet Tiruvadi, MD PhD @vineettiruvadi
Full Stack NeuroTech. AI ∪ Med via Control Theory. Reverse-Engineer. linkedin.com/in/vineet-tiru… Los Angeles/NYC Joined April 2023-
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One of my favorite things about the mathematical perspective is that it always strives to find unity across seeming disparity. x.com/arghya_dutta_/…
One of my favorite things about the mathematical perspective is that it always strives to find unity across seeming disparity. x.com/arghya_dutta_/…
Friendly reminder that vibes are inner products, and some people have excellent basis functions.
Friendly reminder that vibes are inner products, and some people have excellent basis functions.
LLMs can hallucinate and lie. They can be jailbroken by weird suffixes. They memorize training data and exhibit biases. 🧠 We shed light on all of these phenomena with a new approach to AI transparency. 🧵 Website: ai-transparency.org Paper: arxiv.org/abs/2310.01405
this paper on collective intelligence by @drmichaellevin is one of the most mind-blowing things I've read in a while it's full of examples of the under-appreciated intelligence of biological systems, with some deep perspective shifts on our understanding of ourselves thread:
once @ylecun told me (heavily paraphrased), it's not F=ma but \min (F-ma)^2. i didn't realize its importance, but it is perhaps the most enlightning perspective i've ever heard.
Hard to convey just how wrong this is.
Hard to convey just how wrong this is.
Oldies but goldies: Martial Agueh and Guillaume Carlier, Barycenters in the Wasserstein Space, 2011. Defines a notion of averaging of probability distributions which is the solution of a convex program involving optimal transport distances. ceremade.dauphine.fr/~carlier/AC_ba…
I'm super excited this post is out! Activation patching is a crucial mech interp technique, but is deceptively hard to use well. In this informal note we discuss the details of different variants of activation patching, thinking intuitively, and choosing the right metrics.
I'm super excited this post is out! Activation patching is a crucial mech interp technique, but is deceptively hard to use well. In this informal note we discuss the details of different variants of activation patching, thinking intuitively, and choosing the right metrics.
𝗕𝗶𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀: limitations of attractor-based formalisms and the need for transients Interesting ideas here about the role of transients in the dynamics of biological systems. What is the role of "ghost" states? arxiv.org/abs/2404.10369
Thank you @COLM_conf !!! Louder for the people in the back 👏👏👏 (Source: COLM 2024 Reviewing Guidelines)
Today's goal: get fully immersed in HF (finally)
Today's goal: get fully immersed in HF (finally)
Upper bound differential entropy of any random variable X by differential entropy of *any exponential family* for the moment parameter η=E_X[t(x)] where t(x) is the sufficient statistics of the exponential family {p_θ}: H(X) ≤ H(p_θ)=-F^*(E_X[t(x)]) 👉arxiv.org/abs/1612.02954
Orbits. Made with #python, #matplotlib and #numpy.
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Why isn't GNN in high demand in industry? reddit.com/r/MachineLearn…
@PessoaBrain I dare you to go with something like: "FOOLS, I am giving you ONE final chance to rectify your decision not to fund my work of staggering genius."
.@FTC voting to ban noncompete clauses for workers. Chair @linakhanFTC weighed in: "It's going to mean that if somebody has a good idea, they're able to bring that idea to market rather than being locked up."
@vineettiruvadi @RianKMD Was this inspired by that captcha? 😂
@vineettiruvadi @svpino and yet it seems that filtering in signal analysis sense helps as much in AI (as it should) will test someday more as I don't have budget for LLM benchmark comparisons 🙃 I've used method I built for EEG on embeddings and🪄results improved but who cares 🤷
@vineettiruvadi @svpino I’m inclined to agree. I think in the big picture there’s an abundance of well studied flaws in our models that keep us from optimal solutions, but comparing payouts recent changes in architecture around pos embs, activations etc, vs margin of improvements seen with improved…
product managers in eng meetings
@svpino @vineettiruvadi in the case of models of the same class usually better data win e.g. in current LLM wars I guess Vineet speaks about the comparison of models in more theoretically unknown places where the same data or even no data just theory can give vastly different results?
@vineettiruvadi Your argument seems to be "If you use the wrong algorithm, quality data won't matter." Of course, it won't. My point is different: You can't out-train a bad dataset. If your data is bad, a better algorithm won't get you much better results. Any mediocre algorithm will beat a…
LLMs can hallucinate and lie. They can be jailbroken by weird suffixes. They memorize training data and exhibit biases. 🧠 We shed light on all of these phenomena with a new approach to AI transparency. 🧵 Website: ai-transparency.org Paper: arxiv.org/abs/2310.01405
Engineering Protein Assemblies: Envisioning the future workbench for integrating structural biology and engineering #Science #Engineering #Technology #STEM #STEAM Learn more about the proteins here: nature.com/articles/s4156…
this paper on collective intelligence by @drmichaellevin is one of the most mind-blowing things I've read in a while it's full of examples of the under-appreciated intelligence of biological systems, with some deep perspective shifts on our understanding of ourselves thread:
on the same topic (not by me!): terrytao.wordpress.com/2007/05/23/sof… "Soft analysis, hard analysis, and the finite convergence principle"
One fairly basic perspective which has shaped my view of modern analysis is that there can be a world of difference between a property which "just holds", as opposed to "holding uniformly" in some suitable sense.
The biggest issue I see with using LLMs for self-inquiry journaling practice is the fact LLMs do not have a world-state model built in. The human experience has nuances not present in internet text data, and ML is ultimately limited by its training data.
once @ylecun told me (heavily paraphrased), it's not F=ma but \min (F-ma)^2. i didn't realize its importance, but it is perhaps the most enlightning perspective i've ever heard.
Which pendulum are you?
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